The use of geospatial imagery (e.g., satellite imagery) has continued to increase in recent years. As such, high quality geospatial imagery has become increasingly valuable. For example, a variety of different entities (e.g., government entities, corporations, individuals, or others) may utilize satellite imagery. As may be appreciated, the use of such satellite imagery may vary widely such that satellite images may be used for a variety of differing purposes.
Due to the nature of image acquisition, a number of geospatial images may be pieced together to form an orthomosaic of a collection of geospatial images that cover a larger geographic area than may be feasibly covered with a single acquired image. In this regard, it may be appreciated that the images that form such an orthomosaic may be acquired at different times or may be acquired using different collection techniques or parameters. In situations where more than one image is available for a given region of interest on the ground, it may be desirable to use the most recent image. Various artifacts can be introduced when multiple separate images are combined into an orthomosaic.
One such artifact is known as a sliver, which is typically a thin, elongated region of an image that is used in an orthomosaic and may have different characteristics as compared to an adjacent portion in the orthomosaic that is created from a different image. For example, the adjacent image portion may be taken in different lighting conditions (cloudy vs. sunny, different image acquisition angles, different sun angles to the ground, and so forth), different seasons of the year (thus different grass and tree colors and so forth), or other different conditions.
Up until recently, orthomosaic generation has always included manual selection of images by a human operator. Generally, the human operator is tasked with reviewing all available images for an area of interest and choosing images for inclusion in the orthomosaic utilizing what the human operator subjectively determines to be the “best” source images. The subjective determinations of the human operator are often guided by a principle that it is preferential to include as few images in the orthomosaic as possible. The human operator may take steps to remove or reduce the number of slivers. In turn, an orthomosaic may be generated utilizing the human-selected images to form the orthomosaic.
As may be appreciated, this human operator-centric process may be time consuming and costly. Moreover, the image selection is subjective to the human user. Accordingly, recent developments have included fully automated or partially automated orthomosaic generation. Such techniques and algorithms may generate undesirable slivers.
It is against this background that the techniques described herein have been developed.